Companies operating refineries and petrochemical plants typically face tough challenges in today's environment. These challenges may include increasingly complex technologies, a reduction in workforce experience levels, and constantly changing environmental regulations.
Furthermore, as feed and product supplies become more volatile, operators often find it more difficult to make operating decisions that may optimize their approach. This volatility may be unlikely to ease in the foreseeable future, but may represent potential to those companies that can quickly identify and respond to opportunities as they arise.
Outside pressures generally force operating companies to continually increase the usefulness of existing assets. In response, catalyst, adsorbent, equipment, and control system suppliers develop more complex systems that can increase performance. Maintenance and operations of these advanced systems generally requires increased skill levels that may be difficult to develop, maintain, and transfer, given the time pressures and limited resources of today's technical personnel. These increasingly complex systems are not always operated to their highest potential. In addition, when existing assets are operated close to and beyond their design limits, reliability concerns and operational risks may increase.
Plant operators typically respond to the above challenges with one or more strategies, such as, for example, availability risk reduction, working the value chain, and continuous optimization. Availability risk reduction emphasizes achieving adequate plant operations as opposed to maximizing performance. Working the value chain emphasizes improving the match of feed and product mix with asset capabilities and outside demands. Continuous optimization employs tools, systems, and models to continuously monitor and bridge gaps in plant performance.
In a typical data cleansing process, only flow meters are corrected. Data cleansing is performed to correct flow meter calibration and fluid density changes, after which the total error of flow meters in a mass balance envelope is averaged to force a 100% mass balance between the net feed and net product flows. But this conventional data cleansing practice ignores other related process information available (e.g., temperatures, pressures, and internal flows) and does not allow for an early detection of a significant error. Specifically, the errors associated with the flow meters are distributed among the flow meters, and thus it is difficult to detect an error of a specific flow meter.
Typically, plant measurements including sensor data are collected on a continual basis, while laboratory measurements are intermittently sampled and delivered to a laboratory for analysis. Thus, when evaluating plant performance based on the actual operating data, it is often difficult to determine a state of health of the plant operation due to a time lag in receiving plant laboratory data.
In many cases, because the laboratory data is collected at a sparse time interval, such as once a day or week, the laboratory data is unavailable during the interval, and thus becomes outdated. Due to the time to updated laboratory data, the plant operators often use the most recently available set of laboratory data for performance evaluation, and assume that the last laboratory data set is still appropriate for the current operating data. This assumption is frequently misleading and inappropriate because the last laboratory data set may be unreliable at the time of plant performance evaluation.
Therefore, there is a need for an improved data cleansing system and method that performs an early detection and diagnosis of plant operation using environmental factors without substantially relying on laboratory data.